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BMC Genomics

, 14:S12

First Online: 21 January 2013DOI: 10.1186-1471-2164-14-S1-S12

Cite this article as: Wittler, R. BMC Genomics 2013 14Suppl 1: S12. doi:10.1186-1471-2164-14-S1-S12

Abstract

BackgroundStructural variations in human genomes, such as deletions, play an important role in cancer development. Next-Generation Sequencing technologies have been central in providing ways to detect such variations. Methods like paired-end mapping allow to simultaneously analyze data from several samples in order to, e.g., distinguish tumor from patient specific variations. However, it has been shown that, especially in this setting, there is a need to explicitly take overlapping deletions into consideration. Existing tools have only minor capabilities to call overlapping deletions, unable to unravel complex signals to obtain consistent predictions.

ResultWe present a first approach specifically designed to cluster short-read paired-end data into possibly overlapping deletion predictions. The method does not make any assumptions on the composition of the data, such as the number of samples, heterogeneity, polyploidy, etc. Taking paired ends mapped to a reference genome as input, it iteratively merges mappings to clusters based on a similarity score that takes both the putative location and size of a deletion into account.

ConclusionWe demonstrate that agglomerative clustering is suitable to predict deletions. Analyzing real data from three samples of a cancer patient, we found putatively overlapping deletions and observed that, as a side-effect, erroneous mappings are mostly identified as singleton clusters. An evaluation on simulated data shows, compared to other methods which can output overlapping clusters, high accuracy in separating overlapping from single deletions.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2164-14-S1-S12 contains supplementary material, which is available to authorized users.

An erratum to this article is available at http:-dx.doi.org-10.1186-1471-2164-14-S1-S16.

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Author: Roland Wittler

Source: https://link.springer.com/



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